class_docstr: Test the DroppingCurve visualizer
docstr: Test image closeness on a classification dataset with MultinomialNB
docstr_tok: ["Test","image","closeness","on","a","classification","dataset","with","MultinomialNB"]
code: def test_classifier(self):
      """
      Test image closeness on a classification dataset with MultinomialNB
      """
      X, y = self.classification
  
      cv = ShuffleSplit(3, random_state=288)
  
      oz = DroppingCurve(
          KNeighborsClassifier(),
          cv=cv,
          feature_sizes=np.linspace(0.05, 1, 20),
          random_state=42,
      )
  
      oz.fit(X, y)
      oz.finalize()
  
      self.assert_images_similar(oz)
  
imports: import sys
  import pytest
  import numpy
  from unittest.mock import patch
  from tests.base import VisualTestCase
  from sklearn.svm import SVC
  from sklearn.naive_bayes import BernoulliNB, MultinomialNB
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.model_selection import ShuffleSplit, StratifiedKFold
  from sklearn.pipeline import Pipeline
  from yellowbrick.datasets import load_mushroom
  from yellowbrick.exceptions import YellowbrickValueError
  from yellowbrick.model_selection import DroppingCurve, dropping_curve
label_desc: Use image node_id 2 for calling the TestDroppingCurve obj's underlying member method code with example usage: obj.test_classifier() without return types